Zhiquan Lai

According to our database, Zhiquan Lai authored at least 41 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

A Memory-Efficient Hybrid Parallel Framework for Deep Neural Network Training.

Dongsheng Li, Shengwei Li, Zhiquan Lai, Yongquan Fu, Xiangyu Ye, Lei Cai, Linbo Qiao

IEEE Trans. Parallel Distributed Syst., April, 2024

2023

Accelerating GNN Training by Adapting Large Graphs to Distributed Heterogeneous Architectures.

Lizhi Zhang, Kai Lu, Zhiquan Lai, Yongquan Fu, Yu Tang, Dongsheng Li

IEEE Trans. Computers, December, 2023

A Survey on Auto-Parallelism of Large-Scale Deep Learning Training.

Peng Liang, Yu Tang, Xiaoda Zhang, Youhui Bai, Teng Su, Zhiquan Lai, Linbo Qiao, Dongsheng Li

IEEE Trans. Parallel Distributed Syst., August, 2023

Merak: An Efficient Distributed DNN Training Framework With Automated 3D Parallelism for Giant Foundation Models.

Zhiquan Lai, Shengwei Li, Xudong Tang, Keshi Ge, Weijie Liu, Yabo Duan, Linbo Qiao, Dongsheng Li

IEEE Trans. Parallel Distributed Syst., May, 2023

Hierarchical Adaptive Pooling by Capturing High-Order Dependency for Graph Representation Learning.

Ning Liu, Songlei Jian, Dongsheng Li, Yiming Zhang, Zhiquan Lai, Hongzuo Xu

IEEE Trans. Knowl. Data Eng., April, 2023

Compressed Collective Sparse-Sketch for Distributed Data-Parallel Training of Deep Learning Models.

Keshi Ge, Kai Lu, Yongquan Fu, Xiaoge Deng, Zhiquan Lai, Dongsheng Li

IEEE J. Sel. Areas Commun., April, 2023

Automated Tensor Model Parallelism with Overlapped Communication for Efficient Foundation Model Training.

Shengwei Li, Zhiquan Lai, Yanqi Hao, Weijie Liu, Keshi Ge, Xiaoge Deng, Dongsheng Li, Kai Lu

CoRR, 2023

CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms.

Yuanyuan Xiao, Zhiquan Lai, Dongsheng Li

Proceedings of the 2023 International Conference on Power, 2023

Efficient Large Models Fine-tuning on Commodity Servers via Memory-balanced Pipeline Parallelism.

Yujie Liu, Zhiquan Lai, Weijie Liu, Wei Wang, Dongsheng Li

Proceedings of the IEEE International Conference on High Performance Computing & Communications, 2023

Rethinking the Distributed DNN Training Cluster Design from the Cost-effectiveness View.

Zhiquan Lai, Yujie Liu, Wei Wang, Yanqi Hao, Dongsheng Li

Proceedings of the IEEE International Conference on High Performance Computing & Communications, 2023

Communication Analysis for Multidimensional Parallel Training of Large-scale DNN Models.

Zhiquan Lai, Yanqi Hao, Shengwei Li, Dongsheng Li

Proceedings of the IEEE International Conference on High Performance Computing & Communications, 2023

Auto-Divide GNN: Accelerating GNN Training with Subgraph Division.

Hongyu Chen, Zhejiang Ran, Keshi Ge, Zhiquan Lai, Jingfei Jiang, Dongsheng Li

Proceedings of the Euro-Par 2023: Parallel Processing - 29th International Conference on Parallel and Distributed Computing, Limassol, Cyprus, August 28, 2023

Prophet: Fine-grained Load Balancing for Parallel Training of Large-scale MoE Models.

Wei Wang, Zhiquan Lai, Shengwei Li, Weijie Liu, Keshi Ge, Yujie Liu, Ao Shen, Dongsheng Li

Proceedings of the IEEE International Conference on Cluster Computing, 2023

2022

DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation.

Yu Tang, Chenyu Wang, Yufan Zhang, Yuliang Liu, Xingcheng Zhang, Linbo Qiao, Zhiquan Lai, Dongsheng Li

CoRR, 2022

BRGraph: An efficient graph neural network training system by reusing batch data on GPU.

Keshi Ge, Zhejiang Ran, Zhiquan Lai, Lizhi Zhang, Dongsheng Li

Concurr. Comput. Pract. Exp., 2022

Accelerating Sample-based GNN Training by Feature Caching on GPUs.

Yuqi He, Zhiquan Lai, Zhejiang Ran, Lizhi Zhang, Dongsheng Li

Proceedings of the 7th IEEE International Conference on Smart Cloud, SmartCloud 2022, 2022

SCGraph: Accelerating Sample-based GNN Training by Staged Caching of Features on GPUs.

Yuqi He, Zhiquan Lai, Zhejiang Ran, Lizhi Zhang, Dongsheng Li

Proceedings of the IEEE Intl Conf on Parallel & Distributed Processing with Applications, 2022

EmbRace: Accelerating Sparse Communication for Distributed Training of Deep Neural Networks.

Shengwei Li, Zhiquan Lai, Dongsheng Li, Yiming Zhang, Xiangyu Ye, Yabo Duan

Proceedings of the 51st International Conference on Parallel Processing, 2022

S2 Reducer: High-Performance Sparse Communication to Accelerate Distributed Deep Learning.

Keshi Ge, Yongquan Fu, Yiming Zhang, Zhiquan Lai, Xiaoge Deng, Dongsheng Li

Proceedings of the IEEE International Conference on Acoustics, 2022

AutoPipe: A Fast Pipeline Parallelism Approach with Balanced Partitioning and Micro-batch Slicing.

Weijie Liu, Zhiquan Lai, Shengwei Li, Yabo Duan, Keshi Ge, Dongsheng Li

Proceedings of the IEEE International Conference on Cluster Computing, 2022

HPH: Hybrid Parallelism on Heterogeneous Clusters for Accelerating Large-scale DNNs Training.

Yabo Duan, Zhiquan Lai, Shengwei Li, Weijie Liu, Keshi Ge, Peng Liang, Dongsheng Li

Proceedings of the IEEE International Conference on Cluster Computing, 2022

2021

Coordinative Scheduling of Computation and Communication in Data-Parallel Systems.

Dongsheng Li, Zhiyao Hu, Zhiquan Lai, Yiming Zhang, Kai Lu

IEEE Trans. Computers, 2021

EmbRace: Accelerating Sparse Communication for Distributed Training of NLP Neural Networks.

Shengwei Li, Zhiquan Lai, Dongsheng Li, Xiangyu Ye, Yabo Duan

CoRR, 2021

S2 Reducer: High-Performance Sparse Communication to Accelerate Distributed Deep Learning.

Keshi Ge, Yongquan Fu, Zhiquan Lai, Xiaoge Deng, Dongsheng Li

CoRR, 2021

PCGraph: Accelerating GNN Inference on Large Graphs via Partition Caching.

Lizhi Zhang, Zhiquan Lai, Yu Tang, Dongsheng Li, Feng Liu, Xiaochun Luo

Proceedings of the 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), New York City, NY, USA, September 30, 2021

Accelerate Graph Neural Network Training by Reusing Batch Data on GPUs.

Zhejiang Ran, Zhiquan Lai, Lizhi Zhang, Dongsheng Li

Proceedings of the IEEE International Performance, 2021

Hippie: A Data-Paralleled Pipeline Approach to Improve Memory-Efficiency and Scalability for Large DNN Training.

Xiangyu Ye, Zhiquan Lai, Shengwei Li, Lei Cai, Ding Sun, Linbo Qiao, Dongsheng Li

Proceedings of the ICPP 2021: 50th International Conference on Parallel Processing, Lemont, IL, USA, August 9, 2021

HMA: An Efficient Training Method for NLP Models.

Yuetong Yang, Zhiquan Lai, Lei Cai, Dongsheng Li

Proceedings of the ICIAI 2021: 2021 the 5th International Conference on Innovation in Artificial Intelligence, 2021

Prediction of the Cyanobacteria Coverage in Time-series Images based on Convolutional Neural Network.

Xiangyu Ye, Zhiquan Lai, Dongsheng Li

Proceedings of the ICCCV 2021: 4th International Conference on Control and Computer Vision, Macau, SAR, China, August 13, 2021

2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters.

Lizhi Zhang, Zhiquan Lai, Shengwei Li, Yu Tang, Feng Liu, Dongsheng Li

Proceedings of the IEEE International Conference on Cluster Computing, 2021

CASQ: Accelerate Distributed Deep Learning with Sketch-Based Gradient Quantization.

Keshi Ge, Yiming Zhang, Yongquan Fu, Zhiquan Lai, Xiaoge Deng, Dongsheng Li

Proceedings of the IEEE International Conference on Cluster Computing, 2021

2020

ADMMiRNN: Training RNN with Stable Convergence via an Efficient ADMM Approach.

Yu Tang, Zhigang Kan, Dequan Sun, Linbo Qiao, Jingjing Xiao, Zhiquan Lai, Dongsheng Li

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Poster Abstract: Model Average-based Distributed Training for Sparse Deep Neural Networks.

Yuetong Yang, Zhiquan Lai, Lei Cai, Dongsheng Li

Proceedings of the 39th IEEE Conference on Computer Communications, 2020

2019

HPDL: Towards a General Framework for High-performance Distributed Deep Learning.

Dongsheng Li, Zhiquan Lai, Keshi Ge, Yiming Zhang, Zhaoning Zhang, Qinglin Wang, Huaimin Wang

Proceedings of the 39th IEEE International Conference on Distributed Computing Systems, 2019

2017

PoweRock: Power Modeling and Flexible Dynamic Power Management for Many-Core Architectures.

Zhiquan Lai, King Tin Lam, Cho-Li Wang, Jinshu Su

IEEE Syst. J., 2017

A Two-Tiered Defence of Techniques to Prevent SQL Injection Attacks.

Yan Zhu, Guidong Zhang, Zhiquan Lai, Boya Niu, Yongjun Shen

Proceedings of the Innovative Mobile and Internet Services in Ubiquitous Computing, 2017

2015

Latency-aware DVFS for efficient power state transitions on many-core architectures.

Zhiquan Lai, King Tin Lam, Cho-Li Wang, Jinshu Su

J. Supercomput., 2015

2014

A Power Modelling Approach for Many-Core Architectures.

Zhiquan Lai, King Tin Lam, Cho-Li Wang, Jinshu Su

Proceedings of the 2014 10th International Conference on Semantics, 2014

Efficient DVFS to Prevent Hard Faults for Many-Core Architectures.

Zhiquan Lai, Baokang Zhao, Jinshu Su

Proceedings of the Information and Communication Technology, 2014

Rhymes: A shared virtual memory system for non-coherent tiled many-core architectures.

King Tin Lam, Jinghao Shi, Dominic Hung, Cho-Li Wang, Zhiquan Lai, Wangbin Zhu, Youliang Yan

Proceedings of the 20th IEEE International Conference on Parallel and Distributed Systems, 2014

2012

Mining of Attack Models in IDS Alerts from Network Backbone by a Two-stage Clustering Method.

Lin-Bo Qiao, Bo-Feng Zhang, Zhiquan Lai, Jinshu Su

Proceedings of the 26th IEEE International Parallel and Distributed Processing Symposium Workshops & PhD Forum, 2012